{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:03:13Z","timestamp":1761058993101,"version":"3.41.2"},"reference-count":15,"publisher":"Emerald","issue":"10","license":[{"start":{"date-parts":[[2009,10,16]],"date-time":"2009-10-16T00:00:00Z","timestamp":1255651200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,10,16]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>As the conventional multistep\u2010ahead prediction may be unsuitable in some cases, the purpose of this paper is to propose a novel method based on joint probability distributions, which provides the most probable estimation for the predicted trajectory.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>Many real\u2010time series can be modeled in hidden Markov models. In order to predict these time series online, sequential Monte Carlo (SMC) method is applied for joint multistep\u2010ahead prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The data of monthly national air passengers in China are analyzed, and the experimental results demonstrate that the method proposed and the corresponding online algorithms are effective.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>In this paper, SMC method is applied for joint multistep\u2010ahead prediction. However, with the increasing of prediction step, the number of particles is increasing exponentially, which means that the prediction steps cannot be too large.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>A very useful advice for researchers who study time\u2010series forecasts.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>A novel method of multistep\u2010ahead prediction based on joint probability distribution is proposed and SMC method is applied to prediction time series online. This paper is aimed at those researchers who focus on time\u2010series forecasts.<\/jats:p><\/jats:sec>","DOI":"10.1108\/03684920910994349","type":"journal-article","created":{"date-parts":[[2009,11,14]],"date-time":"2009-11-14T07:01:21Z","timestamp":1258182081000},"page":"1819-1827","source":"Crossref","is-referenced-by-count":8,"title":["SMC method for online prediction in hidden Markov models"],"prefix":"10.1108","volume":"38","author":[{"given":"Dongqing","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xuanxi","family":"Ning","sequence":"additional","affiliation":[]},{"given":"Xueni","family":"Liu","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022020220294206100_b4","doi-asserted-by":"crossref","unstructured":"Avrulampalam, S., Maskell, S., Gordon, N. and Clapp, T. 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(2006), \u201cEstimation of nonlinear dynamic systems \u2013 theory and applications\u201d, PhD thesis, Department of Electrical Engineering, Link\u00f6ping University, Link\u00f6ping."},{"key":"key2022020220294206100_b1","doi-asserted-by":"crossref","unstructured":"Storvik, G. (2002), \u201cParticle filters for state space models with the presence of unknown static parameters\u201d, IEEE Transaction on Signal Processing, Vol. 50 No. 2, pp. 281\u20109.","DOI":"10.1109\/78.978383"},{"key":"key2022020220294206100_b9","unstructured":"Zhang, X. 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